Training accurate and compact one-stage object detection networks with heuristic knowledge

Abstract

A training scheme called region-refocusing (RR) is proposed to improve the accuracy and accelerate the convergence of compact one-stage detection neural networks. Main contributions are as follows: (1) the RR mask is first proposed to incorporate the position information and the significance of objects, whereby the regions containing objects can be learned selectively by the compact student detector, which leads to more reasonable feature expressions; (2) within the RR training framework, the selected objectness features from the large teacher detector are utilized to enrich the supervision information and enhance the loss functions for training the student detector, which eventually contributes to rapid convergence and accurate detection; (3) by virtue of the RR scheme, the mean average precision (mAP) of the compact detector can be significantly improved even if the model is initialized from scratch. Superiority of RR has been verified on several benchmark data sets in comparison with other training schemes; the mAP of the well-known tiny-YOLOv2 can be improved from 57.4% to 63.8% by 6.4 points on the VOC2007 test set when the weights are pretrained on ImageNet. Remarkably, when the pretraining process is omitted, it yields a significant boost of mAP by 22.6 points compared with plain training scheme, which demonstrates the robustness and high efficiency of the RR training scheme. Meanwhile, the compact one-stage detector trained with our framework is competent to be deployed on resource-constrained devices for the competitive precision as well as having a lower requirement for computing power.